Object activity modeling method
An object activity modeling method which can efficiently model complex objects such as a human body is provided. The object activity modeling method includes the steps of (a) obtaining an optical flow vector from a video sequence; (b) obtaining the probability distribution of the feature vector for a plurality of video frames, using the optical flow vector; (c) modeling states, using the probability distribution of the feature vector; and (d) expressing the activity of the object in the video sequence based on state transition. According to the modeling method, in video indexing and recognition field, complex activities such as human activities can be efficiently modeled and recognized without segmenting objects.
1. An object activity recognition method comprising the steps of:
(a) obtaining feature vectors by motion estimation for video frames;
(b) determining a state, to which each frame belongs, using the obtained feature vectors; and
(c) determining an activity model, which maximizes the probability between activity models and a video frame provided from a given activity model dictionary using a transition matrix for the determined state, as the recognized activity, wherein the step (c) comprises a step of finding an activity model, which maximizes probability P(O|λ) from the given activity model dictionary {λ 1 , λ 2 , . . . , λ E }, when T is a positive integer indicating the number of frames forming the video sequence, Z 1 , 2 , . . . , Z T are feature vectors of first frame, second frame, . . . , T-th frame, respectively, and if video frame O={Z 1 , 2 , . . . ,Z T } is given and E is the number of state models, and wherein the transition matrix is obtained by using an expectation-maximization (EM) algorithm based on the observation symbol probability {b j (.)} corresponding to scene j in the training process.